Abstract: In order to solve the problem of missed detection easily caused in dense multi-target detection, an infrared small target detection algorithm based on double neighborhood contrast measure was proposed. First, the peak search algorithm was used to screen out the candidate targets; then the candidate targets were traversed through a single-scale three-layer double neighborhood window; finally the dual-neighbor contrast model was used to calculate the minimum gray contrast of the candidate target area, and the contrast and suppresses clutter were enhanced by the diagonal gradient. The results show that compared with the five comparison methods, the background suppression factor and contrast gain of this method are increased by 4.7 times and 1.8 times on average, respectively, which effectively suppresses clutter and enhances the target. This research can accurately detect multiple targets that are close to each other, which is helpful to improve the accuracy of multi-target detection in complex backgrounds.